Offline Tamil Handwritten Character Recognition Using Sub Line Direction and Bounding Box Techniques

Shyni, S. M.; Raj, M. Antony Robert; Abirami, S.

doi:10.17485/ijst/2015/v8is7/67780

Cited by 21 publications

(5 citation statements)

References 11 publications

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“…All a free man direction numbers, slant angle, ratio of components, curvature, linearity, and curliness are the six distinguishing characteristics. In a research described in [56], feature selection as well as extraction were performed in two stages. The features were at first chosen using a zoning strategy that used an 8-direction chain-code process.…”

Section: )mentioning

confidence: 99%

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

Shaik Moinuddin Ahmed

2023

IJRITCC

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The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards.

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Section: )mentioning

confidence: 99%

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

Shaik Moinuddin Ahmed

2023

IJRITCC

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“…Chain code procedure is used with image zoning techniques to represent the boundaries of the characters as a sequence of connected pixels. Then these boundaries are considered as distinct features [37]. Zernike moments captures the orthogonal features by dividing the image into a series of radial and angular polynomial moments.…”

Section: Feature Extractionmentioning

confidence: 99%

Ancient Character Recognition: A Comprehensive Review

Krithiga,

Varsini,

Gabriel Joshua

et al. 2023

IEEE Access

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Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil letters (Vattezhuthu) into modern-day Tamil letter structures. Various methodologies and pre-processing techniques have been used for denoising the ancient Tamil manuscript to retrieve the Tamil text. Researchers have used various synthesized and scanned images of stone wall inscriptions, palm leaves manuscripts, and offline handwritten characters for their analysis. Over the years, Ancient Tamil scripts have deteriorated with time due to various natural calamities. Strong denoising and feature extraction methods are required to separate the letters accurately to tackle this issue. Techniques such as CNN(OCR), ResNet, SVM, KNN, HorVer method, etc., are utilized to digitize Tamil characters. This technique has successfully converted handwritten characters into digitalized text for multiple languages, including Tamil, Arabic, English, Latin, Chinese, German, etc. Different models have been evaluated based on their segmentation and recognition rates, accuracy, detection rate, precision, and confusion matrix. This paper will concentrate on Ancient Tamil character segmentation and recognition models. Besides, we will give an overview of the different models and datasets available. Lastly, we summarise the key challenges and the future scope related to the topic.

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“…It can be defined as a dismantle step of texts image, pseudo images of personal symbols. Writing in Arabic is cursive as a result of that, words and sub words are detached with spaces [7], [8]. For that, the boundaries of words usually characterized by a space.…”

Section: Word Segmentationmentioning

confidence: 99%

Isolated Arabic handwritten words recognition using EHD and HOG methods

Mohammed¹,

Tariq²,

Ayad

2021

IJEECS

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<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>

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Offline Tamil Handwritten Character Recognition Using Sub Line Direction and Bounding Box Techniques

Cited by 21 publications

References 11 publications

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

Ancient Character Recognition: A Comprehensive Review

Isolated Arabic handwritten words recognition using EHD and HOG methods

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